Universe, ecosystem, social system, etc. are evolving systems. The evolving processes of these systems have gradual small changes and rapid drastic changes with uncertainties under the constraints of environment. Systems, as a whole, are evolving toward complexity, diversity and variety with fluctuations and jumps. New order emerges from “mutations”. The evolution is both chance-dependent and path-dependent. In this paper three basics: entropy, information and noise are emphasized with regard to system evolution which is a field that can give people wisdom to solve system problems with domain knowledge.

A case is made for further developing a branch of systems engineering that focuses on problems and issues which arise in the service sector. We promulgate this special focus not only because of the size and importance of the service sector but also because of the unique opportunities that systems engineering can exploit in the design and joint production and delivery of services. We begin by considering the economic, technological and demographic contexts within which the service sector has flourished; we then address both services, especially emerging services, and systems engineering, followed by a discussion of how to advance the field of service systems engineering, and concluding with several remarks. In particular, a number of service systems engineering methods are identified to enhance the design and production/delivery of services, especially taking advantage of the unique features that characterize services — namely, services, especially emerging services, are information-driven, customer-centric, e-oriented, and productivity-focused.

Two heuristics, the max-min approach and the Nakagawa and Nakashima method, are considered for the redundancy allocation problem with series-parallel structure. The max-min approach can formulate the problem as an integer linear programming problem instead of an integer nonlinear problem. This paper presents a comparison between those methods from the standpoint of solution quality and computational complexity. The experimental results show that the max-min approach is superior to the Nakagawa and Nakashima method in terms of solution quality in small-scale problems, but analysis of computational complexity shows that the max-min approach is inferior to other greedy heuristics.

This paper first introduces a new discipline knowledge science and the role of systems science in its development. Then, after the discussion on current trend in systems science, the paper proposes a new systems methodology for knowledge management and creation. Finally, the paper discusses mathematical modeling techniques to represent and manage human knowledge that is essentially vague and context-dependent.

In real world situations, most scheduling problems occur neither as complete off-line nor as complete on-line models. Most likely, a problem arises as an on-line model with some partial information. In this article, we consider such a model. We study the scheduling problem P(n ₁,n ₂), where two groups of jobs are to be scheduled. The first job group is available beforehand. As soon as all jobs in the first group are assigned, the second job group appears. The objective is to minimize the longest job completion time (makespan). We show a lower bound of 3/2 even for very special cases. Best possible algorithms are presented for a number of cases. Furthermore, a heuristic is proposed for the general case. The main contribution of this paper is to discuss the impact of the quantity of available information in designing an on-line algorithm. It is interesting to note that the absence of even a little bit information may significantly affect the performance of an algorithm.

This paper addresses the problem of handling the uncertainty of demand in a one-supplier-one-retailer supply chain system. Demand variation often makes the real production different from what is originally planned, causing a deviation cost from the production plan. Assume the market demand is sensitive to the retail price in a nonlinear form, we show how to effectively handle the demand uncertainty in a supply chain, both for the case of centralized-decision-making system and the case of decentralized-decision-making system with perfect coordination.

A heuristic approach is developed for supply chain planning modeled as multi-item multi-level capacitated lot sizing problems. The heuristic combines Lagrangian relaxation (LR) with local search. Different from existing LR approaches that relax capacity constraints and/or inventory balance constraints, our approach only relaxes the technical constraints that each 0-1 setup variable must take value 1 if its corresponding continuous variable is positive. The relaxed problem is approximately solved by using the simplex algorithm for linear programming, while Lagrange multipliers are updated by using a surrogate subgradient method that ensures the convergence of the dual problem in case of the approximate resolution of the relaxed problem. At each iteration, a feasible solution of the original problem is constructed from the solution of the relaxed problem. The feasible solution is further improved by a local search that changes the values of two setup variables at each time. By taking the advantages of a special structure of the lot-sizing problem, the local search can be implemented by using a modified simplex algorithm, which significantly reduces its computation time. Numerical experiments show that our approach can find very good solutions for problems of realistic sizes in a short computation time and is more effective than an existing commercial optimization code.

Enterprise systems must have the structure to adapt the change of business environment. When rebuilding enterprise system to meet the extended operational boundaries, the concept of IT city planning is applicable and effective. The aim of this paper is to describe the architectural approach from the integrated information infrastructure (In3) standpoint and to propose for applying the “City Planning” concept for rebuilding “inter-application spaghetti” enterprise systems. This is mainly because the portion of infrastructure has increased with the change of information systems from centralized systems to distributed and open systems. As enterprise systems have involved heterogeneity or architectural black box in them, it may be required the integration framework (meta-architecture) as a discipline based on heterogeneity that can provide comprehensive view of the enterprise systems. This paper proposes “EII Meta-model” as the integration framework that can optimize the overall enterprise systems from the IT city planning point of view. EII Meta-model consists of “Integrated Information Infrastructure Map (In3-Map)”, “Service Framework” and “IT Scenario”. It would be applicable and effective for the viable enterprise, because it has the mechanism to adapt the change. Finally, we illustrate a case of information system in an online securities company and demonstrate applicability and effectiveness of EII Meta-model to meet their business goals.